Dynamic Data Reallocation for Skew Management inShared-Nothing Parallel Databases

Abstract

The shared nothing parallel database architecture is gaining wide popularity due to its scalability and increased data availability. However, in order to efficiently utilize parallelism in such architecture, independent data sets must be assigned to different processing nodes. This, of course, can initially be achieved by employing a careful partitioning scheme that allocates disjoint data sets to different processors. However, variations in the data access pattern may render some processors overloaded while others underloaded. This skewness in data access decreases the effective parallelism and eventually leads to overall performance degradation. A number of solutions have been proposed to periodically perform data re-allocation to remove the skewness in data access. Most of the proposed solutions perform either static re-allocation that requires the system to be taken off-line or dynamic, but non-transactional, re-allocation. In this paper, we introduce a dynamic and transactional re-allocation scheme based on the work on disk cooling in shared memory architecture by Scheuermann et al. The proposed scheme enhances the effective parallelism in the system regardless of the variations in the pattern of access. The proposed scheme detects access skew as it occurs and re-allocates data partitions to underloaded processing elements on the fly. Only the block being moved becomes unavailable. In addition, mutual consistency among transactions concurrent to the re-allocation event is preserved. The proposed scheme also uses replication as an additional cooling mechanism to help distribute access load over multiple replicas. We conducted a series of simulation experiments to study the behavior of shared nothing parallel database systems with and without the proposed dynamic re-allocation scheme. We also experimented with several replication strategies to measure their impact on the system performance. Finally, we studied the effect of using different concurrency control strategies on the efficiency of dynamic re-allocation.